Abstract
Modern technological systems increasingly rely on complex interactions between vastly different parties, such as human users, automated processes (e.g., AI), and institutions. This creates a broad sociotechnical problem: systems are often designed without adequately supporting the user’s ability to understand and respond to curveballs in the technology or surrounding environment. This problem matters because failures in usability and accessibility can significantly impact safety, user satisfaction, and quality of life. My technical and STS research projects both address this issue. The technical project focuses on improving indoor navigation for elderly and disabled patients in hospital environments, while the STS project analyzes how failures to support user understanding of a system contributed to the Boeing 737 Max disasters. Together, these projects argue that technological systems must be designed not only for functionality, but for the user’s ability to understand, navigate, and respond to them.
My technical research investigated the problem of hospital navigation, particularly for patients who struggle to find their way through large and complex healthcare facilities. My capstone team observed a recurring issue in which patients miss or arrive late to appointments due to confusing layouts, poor signage, and limited accessibility accommodations. To address this, we developed a hand-held navigational device designed to guide users from the welcome desk to their destination. It is essentially an indoor compass that directs a patient to their next destination. Our design emphasized clarity, simplicity, and accessibility, ensuring that users with limited mobility or technological familiarity could effectively use the device. Through iterative prototyping and testing, we identified key challenges such as location accuracy and choke points in a hospital which could confuse our system, and the patient by proxy. Our preliminary findings suggest that real-time, indoor navigation significantly improves users’ ability to move through challenging environments. This project contributes to the broader sociotechnical problem by demonstrating how intentional system design can enhance usability and reduce barriers to access in critical settings like healthcare. In other words, engineers should design a system with the end user in mind every step of the way, so that the user never feels like they don’t have the knowledge or skills to succeed.
My STS research examined the Boeing 737 Max disasters as a case study of sociotechnical failure, focusing on how breakdowns in communication, training, and regulatory oversight prevented pilots from effectively responding to system malfunctions. Using Actor-Network Theory (ANT), I analyzed the interactions between Boeing, the Federal Aviation Administration (FAA), pilots, and the MCAS software system. I drew from sources including investigative journalism, regulatory reports, and academic case studies to understand how information and responsibility were distributed across the network. My analysis found that Boeing, as the primary network builder, failed to properly communicate critical system information, while the FAA did not provide sufficient independent oversight and enforce accountability. Most importantly, pilots were excluded from meaningful participation in the system because they were not informed about MCAS or trained to respond to its failures. As a result, when the system malfunctioned, pilots lacked the ability to understand their situation. This project demonstrates that technological failures are often rooted not only in design flaws but from the poor relationships between actors of a sociotechnical network.
While my research contributes to addressing this broader sociotechnical problem, it also has limitations. The technical project demonstrates the feasibility of a checkpoint-based indoor navigation system but does not fully address challenges related to interference, accuracy in choke points, cost, or changing hospital systems. Additionally, testing was limited and may not fully capture the diversity of patient needs. Meanwhile, the STS project mainly provides an analysis of one high-profile case but may not capture all nuances of sociotechnical breakdown in other domains. Future research should explore updateable implementation of indoor navigation systems and scrutinizing where our system fails to identify common patterns in how users feel excluded by their technology. A machine learning solution is a great avenue to solve these problems. Additionally, it will be valuable to assess other cases where the users of a particular technology struggle with the lack of information they have been given.
I would like to thank my fourth year STS professors, Benjamin Laugelli and Caitlin Wylie, for their guidance and support throughout this endeavour. I am also grateful to the faculty of the Electrical and Computer Engineering department, notably Caroline Crockett and Keith Williams, for providing valuable feedback and resources. Finally, I would like to thank my peers for their encouragement and constructive advice as I worked on my thesis.
